Description Usage Arguments Value Author(s) References See Also Examples

These are the basic computing engines called by `RLM`

used to fit robust linear models. These should not be used
directly unless by experienced users.

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`x` |
design matrix of dimension n * p. |

`y` |
vector of observations of length n, or a matrix with n rows. |

`maxit` |
the limit on the number of IWLS iterations. |

`k` |
tuning constant used for Huber proposal 2 scale estimation. |

`offset` |
numeric of length n. This can be used to specify an a priori known component to be included in the linear predictor during fitting. |

`method` |
currently, only method="rlm.fit" is supported. |

`cov.formula` |
are the methods to compute covariance matrix, currently either weighted or asymptotic. |

`start` |
vector containing starting values for the paramter estimates. |

`error.limit` |
the convergence criteria during iterative estimation. |

a list with components

`coeffecients ` |
p vector |

`Std.Error ` |
p vector |

`t.value ` |
p vector |

`cov.matrix ` |
matrix of dimension p*p |

`res.SD ` |
value of residual standard deviation |

...

Stefano Calza <stefano.calza@biostatistics.it>, Suo Chen and Yudi Pawitan.

Yudi Pawitan: In All Likelihood: Statistical modeling and inference using likelihood. Oxford University Press. 2001.

`RLM`

which you should use for robust linear regression usually.

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